Importing Libraries that are generally required

library(dplyr)

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library(ggplot2)

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library(plotly)

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library(xts)

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library(dygraphs)

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library(maps)

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library(gapminder)

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library(usmap)

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library(tibble)

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library(maps)
library(mapproj)

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data("statepop")

## Make the state names lower-cased
statepop$region <- tolower(statepop$full)
library(ggplot2)
states_map <- map_data("state")
## Merge
population_map <- merge(states_map, statepop, by= "region", all.x=T)

population_states<-ggplot(population_map, aes(x = long, y = lat, group = group, fill = pop_2022)) +
              geom_polygon(colour = "white") 
population_states

marketing_data<-read.csv("E:\\MBA-MAR--657-Visual-Analytics\\Datasets\\marketing_campaign.csv")
head(marketing_data,2)

##     ID Year_Birth  Education Marital_Status Income Kidhome Teenhome Dt_Customer
## 1 5524       1957 Graduation         Single  58138       0        0    9/4/2012
## 2 2174       1954 Graduation         Single  46344       1        1    3/8/2014
##   Recency MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts
## 1      58      635        88             546             172               88
## 2      38       11         1               6               2                1
##   MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases
## 1           88                 3               8                  10
## 2            6                 2               1                   1
##   NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5
## 1                 4                 7            0            0            0
## 2                 2                 5            0            0            0
##   AcceptedCmp1 AcceptedCmp2 Complain Response
## 1            0            0        0        1
## 2            0            0        0        0

sweet_vs_fish_plot <- plot_ly(
  marketing_data,
  x = ~MntSweetProducts,
  y = ~MntFishProducts,
  color = ~Education,           
  size = ~NumStorePurchases,     
  type = 'scatter',
  mode = 'markers',
  hoverinfo = 'text',
  text = ~paste(
    "Sweet Spend: $", MntSweetProducts,
    "<br>Fish Spend: $", MntFishProducts,
    "<br>Store Purchases: ", NumStorePurchases,
    "<br>Education:", Education
  )
) %>%
  layout(
    title = "Sweet vs Fish Product Spending by Education and Marital Status",
    xaxis = list(title = "Spending on Sweet Products (USD)"),
    yaxis = list(title = "Spending on Fish Products (USD)"),
    legend = list(title = list(text='Education'))
  )

sweet_vs_fish_plot

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marketing_campaign <- marketing_data %>%
  mutate(Age = 2025 - Year_Birth,
         AgeGroup = cut(Age, breaks = c(18,30,40,50,60,70,100),
                        labels = c("18-30","31-40","41-50","51-60","61-70","70+")),
         Total_Spend = MntWines + MntFruits + MntMeatProducts +
                       MntFishProducts + MntSweetProducts + MntGoldProds)

edu_spend <- marketing_campaign %>%
  group_by(Education, AgeGroup) %>%
  summarise(Avg_Spend = mean(Total_Spend, na.rm = TRUE),
            Avg_Income = mean(Income, na.rm = TRUE),
            .groups = 'drop')

anim_spend <- ggplot(edu_spend, aes(x = Avg_Income, y = Avg_Spend, 
                                    color = Education,
                                    frame = AgeGroup)) +
  geom_point(alpha =2) +
  labs(title = "Animated Average Spending by Education Level and Age Group",
       x = "Average Income (USD)", y = "Average Spending (USD)")

anim_spend <- ggplotly(anim_spend)

anim_spend

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